CCDL 2020
We’ll use the same gene expression dataset we used in the previous notebook. It is a pre-processed astrocytoma microarray dataset that we performed a set of differential expression analyses on.
More tidyverse resources:
- R for Data Science
- tidyverse documentation
- Cheatsheet of tidyverse data transformation
- Online tidyverse book chapter
The tidyverse is a collection of packages that are handy for general data wrangling, analysis, and visualization. Other packages that are specifically handy for different biological analyses are found on Bioconductor. If we want to use a package’s functions we first need to install them.
TODO: Language around Docker container may change depending on RStudio cloud stuff
In our Docker container, we already have the tidyverse and other packages we will use in this workshop installed for you. But if you needed to install it or other packages available on CRAN, you do it using the install.packages() function like this: install.packages("tidyverse").
library(tidyverse)Registered S3 method overwritten by 'dplyr':
method from
print.rowwise_df
[37m── [1mAttaching packages[22m ───────────────────────────────────────────────── tidyverse 1.2.1 ──[39m
[37m[32m✔[37m [34mggplot2[37m 3.2.0 [32m✔[37m [34mpurrr [37m 0.3.2
[32m✔[37m [34mtibble [37m 2.1.3 [32m✔[37m [34mdplyr [37m 0.8.3
[32m✔[37m [34mtidyr [37m 0.8.3 [32m✔[37m [34mstringr[37m 1.4.0
[32m✔[37m [34mreadr [37m 1.3.1 [32m✔[37m [34mforcats[37m 0.4.0[39m
[37m── [1mConflicts[22m ──────────────────────────────────────────────────── tidyverse_conflicts() ──
[31m✖[37m [34mdplyr[37m::[32mfilter()[37m masks [34mstats[37m::filter()
[31m✖[37m [34mdplyr[37m::[32mlag()[37m masks [34mstats[37m::lag()[39m::Note that if we had not imported the tidyverse set of packages using library() like above, and we wanted to use a tidyverse function like read_tsv(), we would need to tell R what package to find this function in. To do this, we would use :: to tell R to load in this function from the readr package by using readr::read_tsv(). You will see this :: method of referencing libraries within packages throughout the course. We like to use it in part to remove any ambiguity in which version of a function we are using; it is not too uncommon for different packages to use the same name for very different functions!
Before we can import the data we need, we should double check where R is looking for files, aka the current working directory. We can do this by using the getwd() function, which will tell us what folder we are in.
# Let's check what directory we are in:
getwd()[1] "/home/rstudio/kitematic/training-modules/intro-to-R-tidyverse"For Rmd files, the working directory is wherever the file is located, but commands executed in the console may have a different working directory.
We will want to make a directory for our output and we will call this directory: results. But before we create the directory, we should check if it already exists. We will show two ways that we can do this.
First, we can use the dir() function to have R list the files in our working directory.
# Let's check what files are here
dir() [1] "00a-rstudio_guide.md" "00b-debugging_resources.md"
[3] "01-introduction-base-R.nb.html" "01-introduction-base-R.Rmd"
[5] "02-intro_to_ggplot2.nb.html" "02-intro_to_ggplot2.Rmd"
[7] "03-intro_to_tidyverse.nb.html" "03-intro_to_tidyverse.Rmd"
[9] "04a-intro_to_R_exercise.nb.html" "data"
[11] "diagrams" "metadata.tsv"
[13] "plots" "README.md"
[15] "refine.bio" "screenshots"
[17] "scripts" This shows us there is no folder called “results” yet.
If we want to more pointedly look for “results” in our working directory we can use the dir.exists() function.
# Check if the results directory exists
dir.exists("results")[1] FALSEIf the above says FALSE that means we will need to create a results directory using the function dir.create().
# Make a directory within the working directory called 'results'
dir.create("results")After creating the results directory above, let’s re-run dir.exists() to see if now it exists.
# Re-check if the results directory exists
dir.exists("results")[1] TRUEWe can use the output of dir.exists() to automatically create or hold off on creating a directory by putting this together in an if statement like below. An if statement has two main parts: First, the test, which is an expression that will result in either TRUE or FALSE. This is put in parenthesis immediately after the if. The next part is the body, which is the commands that will be executed if the test is TRUE. These are placed within a set of braces { }. Note that we used an exclamation point in the test to signify that we want a directory to be created only if dir.exists(results) is NOT equal to TRUE.
# If 'results' directory doesn't exist...
if (!dir.exists("results")) {
# ... create a 'results' directory
dir.create("results")
}The dir.exists() function will not work on files themselves. In that case, there is an analogous function called file.exists().
Try using the file.exists() function to see if the file gene_results_GSE44971.tsv exists in the current directory. Use the code chunk we set up for you below. Note that in our notebooks (and sometimes elsewhere), wherever you see a <FILL_IN_THE_BLANK> like in the chunk below, that is meant for you to replace (including the angle brackets) with the correct phrase before you run the chunk (otherwise you will get an error).
# Replace the <PUT_FILE_NAME_HERE> with the name of the file you are looking for
# Remember to use quotes to make it a character string
file.exists(<PUT_FILE_NAME_HERE>)Now that we’ve determined that gene_results_GSE44971.tsv exists, we are ready to read it into our R environment.
Declare the name of the directory where we will read in the data.
data_dir <- "data"Although base R has functions to read in data files, the functions in the readr package (part of the tidyverse) are faster and more straightforward to use so we are going to use those here. Because the file we are reading in is a TSV (tab separated values) file we will be using the read_tsv function. There are analogous functions for CSV (comma separated values) files (read_csv()) and other files types.
stats_df <- readr::read_tsv(file.path(data_dir,
"gene_results_GSE44971.tsv"))Parsed with column specification:
cols(
ensembl_id = [31mcol_character()[39m,
gene_symbol = [31mcol_character()[39m,
contrast = [31mcol_character()[39m,
log_fold_change = [32mcol_double()[39m,
avg_expression = [32mcol_double()[39m,
t_statistic = [32mcol_double()[39m,
p_value = [32mcol_double()[39m,
adj_p_value = [32mcol_double()[39m
)Following the template of the previous chunk, use this chunk to read in the file GSE44971.tsv that is in the data folder.
# Use this chunk to read in data from the file `GSE44971.tsv`Use this chunk to explore what gene_df looks like.
# Explore `gene_df`What information is contained in gene_df?
One nifty feature of the tidyverse is pipes: %>% These handy things allows you to funnel the result of one expression to the next, making your code a little more streamlined.
For example, the output from this:
filter(stats_df, contrast == "male_female")…is the same as the output from this:
stats_df %>% filter(contrast == "male_female")This can make your code cleaner and easier to follow a series of related commands. Let’s look at an example with our stats of of how the same functions look with or without pipes:
Example 1: without pipes:
stats_arranged <- arrange(stats_df, t_statistic)
stats_filtered <- filter(stats_arranged, avg_expression > 50)
stats_nopipe <- select(stats_filtered, contrast, log_fold_change, p_value)UGH, we have to keep track of all of those different intermediate data frames and type their names so many times here! We could maybe streamline things by using the same variable name at each stage, but even then there is a lot of extra typing, and it is easy to get confused about what has been done where. It’s annoying and makes it harder for people to read.
Example 2: Same result as 1 but with pipes!
# Example of the same modifications as above but with pipes!
stats_pipe <- stats_df %>%
arrange(t_statistic) %>%
filter(avg_expression > 50) %>%
select(contrast, log_fold_change, p_value)What the %>% (pipe) is doing here is feeding the result of the expression on its left into the first argument of the next function (to its right, or on the next line here). We can then skip that first argument (the data in these cases), and move right on to the part we care about at that step: what we are arranging, filtering, or selecting in this case.
Let’s double check that these are the same by using the function, all.equal().
all.equal(stats_nopipe, stats_pipe)[1] TRUEall.equal() is letting us know that these two objects are the same.
Now that hopefully you are convinced that the tidyverse can help you make your code neater and easier to use and read, let’s go through some of the popular tidyverse functions and so we can create pipelines like this.
Let’s say we wanted to filter this gene expression dataset to particular sample groups. In order to do this, we would use the function filter() as well as a logic statement (usually one that refers to a column or columns in the data frame).
# Here let's filter the data to a particular gene
stats_df %>%
filter(gene_symbol == "SNCA")We can use filter() similarly for numeric statements.
# Here let's filter the data to have average expression values above 50
stats_df %>%
filter(avg_expression > 50)We can apply multiple filters at once, which will require all of them to be satisfied for every row in the results:
stats_df %>%
filter(contrast == "male_female",
avg_expression > 50)When we are filtering, the %in% operator can come in handy if we have multiple items we would like to match. Let’s take a look at what using %in% does.
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[ reached getOption("max.print") -- omitted 5804 entries ]%in% returns a logical vector that now we can use in dplyr::filter.
stats_df %>%
filter(gene_symbol %in% c("SNCA", "CDKN1A"))Let’s return to our first filter() and build on to it. This time, let’s keep only some of the columns from the data frame using the select() function. Let’s also save this as a new data frame called stats_filtered_df.
stats_filtered_df <- stats_df %>%
filter(contrast == "male_female",
avg_expression > 50) %>%
select(log_fold_change, t_statistic)Let’s say we wanted to arrange this dataset so that the genes are arranged by the smallest p values to the largest. In order to do this, we would use the function arrange() as well as the column we would like to sort by (in this case p_value).
stats_df %>%
arrange(p_value) What if we want to sort from largest to smallest? Like if we want to see the genes with the highest average expression? We can use the same function, but instead use the desc() function and now we are using avg_expression column.
stats_df %>%
arrange(desc(avg_expression))What if we would like to create a new column of values? For that we use mutate() function.
stats_df %>%
mutate(log10_p_value = -log10(p_value))What if we want to obtain summary statistics for a column? The summarize function allows us to calculate summary statistics for a column. Here we will use summarize to obtain an mean log folder change over all the genes.
stats_df %>%
summarize(mean(log_fold_change))What if we’d like to obtain a summary statistics but have them for various groups? Conveniently named, there’s a function called group_by() that seamlessly allows us to do this. Also note that group_by() allows us to group by multiple variables at a time if you want to.
stats_summary_df <- stats_df %>%
group_by(contrast) %>%
summarize(mean(log_fold_change))Let’s look at a preview of what we made:
stats_summary_dfHere we have the mean log fold change expression per each contrast we made.
apply family of functionsIn base R, the apply family of functions can be an alternative methods for performing transformations across a data frame, matrix or other object structures.
One of this family is (shockingly) the function apply(), which operates on matrices or data frames. The first argument to apply() is the data object we want to work on. The third argument is the function we will apply to each row or column of the data object. The second argument in specifies whether we are applying the function across rows or across columns (1 for rows, 2 for columns).
Remember that gene_df is a gene x sample gene expression data frame.
# Calculate row means
gene_means <- apply(gene_df[, -1], 1, mean) # Notice we are using 1 hereError in apply(gene_df[, -1], 1, mean) : object 'gene_df' not foundWhy do we have a [, -1] after gene_df in the above chunk?
Now let’s investigate the same set up, but use 2 to apply over the columns of our matrix.
rr # Calculate sample means sample_means <- apply(gene_df[, -1], 2, mean) # Notice we use 2 here
sample_means be?length(sample_means)
[1] 58Although the apply functions may not be as easy to use as the tidyverse functions, for some applications, apply methods can be better suited. In this workshop, we will not delve too deeply into the various other apply functions (tapply(), lapply(), etc.) but you can read more examples about them here.
Let’s say we have a scenario where we have two data frames that we would like to combine. Recall that stats_df and gene_df are data frames that contain information about some of the same genes. The dplyr::join family of functions are useful for various scenarios of combining data frames.
For now, we will focus on inner_join(), which will combine data frames by only keeping information about matching rows that are in both data frames. We need to use the by argument to designate what column(s) should be used as a key to match the data frames. In this case we want to match the gene information between the two, so we will specify that we want to compare values in the ensembl_id column from stats_df to the Gene column from gene_df.
rr stats_df %>% inner_join(gene_df, by = c(‘ensembl_id’ = ‘Gene’))
Let’s write some of the data frames we created to a file. To do this, we can use the readr library of _write() functions. The first argument of write_tsv() is the data we want to write, and the second argument is a character string that describes the path to the new file we would like to create. Remember that we created a results directory to put our output in, but if we want to save our data to a directory other than our working directory, we need to specify this. This is what we will use the file.path() function for. Let’s look in a bit more detail what file.path() does, by examining the results of the function in the examples below.
rr # Which of these file paths is what we want to use to save our data to the # results directory we created at the beginning of this notebook? file.path(-install, _summary.tsv)
[1] \docker-install/stats_summary.tsv\rr file.path(, _summary.tsv)
[1] \results/stats_summary.tsv\rr file.path(_summary.tsv, )
[1] \stats_summary.tsv/results\Replace <NEW_FILE_PATH> below with the file.path() statement from above that will successfully save our file to the results folder
# Write our data frame to a TSV file
readr::write_tsv(stats_summary_df, <NEW_FILE_PATH>)Check in your results directory to see if your new file has successfully saved.
For this example we have been working with data frames, which are conveniently represented as TSV or CSV tables. However, in other situations we may want to save more complicated or very large data structures, RDS (R Data Serialized/Single) files may be a better option for saving our data. RDS is R’s special file format for holding data exactly as you have it in your R environment. RDS files can also be compressed, meaning they will take up less space on your computer. Let’s save our data to an RDS file in our results folder. You will need to replace the .tsv with .RDS, but you can use what we determined as our file path for the last chunk as your template.
# Write your object to an RDS file
readr::write_rds(stats_summary, <PUT_CORRECT_FILE_PATH_HERE>)Since now you have learned the readr functions: read_tsv(), write_tsv(), and now, write_rds(), what do you suppose the function you will need to read your RDS file is called? Use that function here to re-import your data in the chunk we set up for you below.
# Read in your RDS file
reimport_df <- <PUT_FUNCTION_NAME>(file.path("results", "stats_clean.RDS"))As is good practice, we will end this session by printing out our session info.
rr # Print out the versions and packages we are using in this session sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 9 (stretch)
Matrix products: default
BLAS/LAPACK: /usr/lib/libopenblasp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] patchwork_1.0.0.9000 maftools_2.2.10 forcats_0.4.0
[4] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2
[7] readr_1.3.1 tidyr_0.8.3 tibble_2.1.3
[10] ggplot2_3.2.0 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 wordcloud_2.6 xfun_0.8
[4] splines_3.6.0 haven_2.1.1 lattice_0.20-38
[7] colorspace_1.4-1 generics_0.0.2 htmltools_0.3.6
[10] yaml_2.2.0 base64enc_0.1-3 survival_2.44-1.1
[13] rlang_0.4.0 pillar_1.4.2 glue_1.3.1
[16] withr_2.1.2 RColorBrewer_1.1-2 modelr_0.1.4
[19] readxl_1.3.1 munsell_0.5.0 gtable_0.3.0
[22] cellranger_1.1.0 rvest_0.3.4 evaluate_0.14
[25] knitr_1.23 broom_0.5.2 Rcpp_1.0.1
[28] backports_1.1.4 scales_1.0.0 jsonlite_1.6
[31] hms_0.4.2 digest_0.6.20 stringi_1.4.3
[34] grid_3.6.0 cli_1.1.0 tools_3.6.0
[37] magrittr_1.5 lazyeval_0.2.2 crayon_1.3.4
[40] pkgconfig_2.0.2 Matrix_1.2-17 data.table_1.12.2
[43] xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.1
[46] rmarkdown_1.13 httr_1.4.0 rstudioapi_0.10
[49] R6_2.4.0 nlme_3.1-140 compiler_3.6.0